fix bugs and ad more info for install

README.md

@@ -1,5 +1,5 @@
-<h1 align="center">✨ ACE-Step ✨</h1>
-<h1 align="center">🎵 A Step Towards Music Generation Foundation Model 🎵</h1>
+<h1 align="center">ACE-Step</h1>
+<h1 align="center">A Step Towards Music Generation Foundation Model</h1>
 <p align="center">
     <a href="https://ace-step.github.io/">Project</a> |
     <a href="https://huggingface.co/ACE-Step/ACE-Step-v1-3.5B">Checkpoints</a> |
@@ -8,7 +8,7 @@
 
 ---
 <p align="center">
-    <img src="./fig/orgnization_logos.png" width="100%" alt="Org Logo">
+    <img src="./fig/whiteboard_exported_image.png" width="100%" alt="StepFun Logo">
 </p>
 
 ## Table of Contents
@@ -113,37 +113,6 @@ Rather than building yet another end-to-end text-to-music pipeline, our vision i
 - 🎵 Takes a vocal track and specified style as input to produce a complete vocal accompaniment
 - 🎸 Creates full instrumental backing that complements the input vocals, making it easy to add professional-sounding accompaniment to any vocal recording
 
-## 💻 Installation
-
-### For MacOS/Linux
-
-```bash
-conda create -n ace_step python==3.10
-conda activate ace_step
-# Install other requirements
-pip install -r requirements.txt
-
-# Install ffmpeg
-conda install ffmpeg
-```
-
-### Windows
-
-```bash
-conda create -n ace_step python==3.10
-conda activate ace_step
-
-# Install PyTorch, TorchAudio, and TorchVision for Windows
-# replace cu121 with your CUDA version
-# replace torchvision and torchaudio with your version
-pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
-
-# Install other requirements
-pip install -r requirements_win.txt
-
-# Install ffmpeg
-conda install ffmpeg
-```
 
 ## 🖥️ Hardware Performance
 
@@ -152,11 +121,78 @@ We have evaluated ACE-Step across different hardware setups, yielding the follow
 | Device | 27 Steps | 60 Steps |
 |--------|----------|----------|
 | NVIDIA A100 | 27.27x | 12.27x |
-| MacBook M2 Max | 2.27x | 1.03x |
 | NVIDIA RTX 4090 | 34.48x | 15.63x |
+| NVIDIA RTX 3090 | 12.76X | 6.48X |
+| MacBook M2 Max | 2.27x | 1.03x |
 
 We use RTF (Real-Time Factor) to measure the performance of ACE-Step. Higher values indicate faster generation speed. 27.27x means to generate 1 minute of music, it takes 2.2 seconds (60/27.27). The performance is measured on a single GPU with batch size 1 and 27 steps.
 
+
+## 💻 Installation
+
+### Prerequisites
+
+* Make sure you have Python installed. You can download it from [python.org](https://www.python.org/).
+* You will also need either Conda (recommended for complex dependencies) or ensure your Python installation includes `venv`.
+
+### Environment Setup
+
+It is highly recommended to use a virtual environment to manage project dependencies and avoid conflicts. Choose **one** of the following methods (Conda or venv):
+
+#### Option 1: Using Conda
+
+1.  **Create the environment** named `ace_step` with Python 3.10:
+    ```bash
+    conda create -n ace_step python=3.10 -y
+    ```
+
+2.  **Activate the environment:**
+    ```bash
+    conda activate ace_step
+    ```
+
+#### Option 2: Using venv
+
+1.  **Ensure you are using the correct Python version.
+
+2.  **Create the virtual environment** (commonly named `venv`):
+    ```bash
+    python -m venv venv 
+    ```
+
+3.  **Activate the environment:**
+    * **On Windows (cmd.exe):**
+        ```bash
+        venv\Scripts\activate.bat
+        ```
+    * **On Windows (PowerShell):**
+        ```powershell
+        .\venv\Scripts\Activate.ps1 
+        ```
+        *(If you encounter execution policy errors, you might need to run `Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process` first)*
+    * **On Linux / macOS (bash/zsh):**
+        ```bash
+        source venv/bin/activate
+        ```
+
+3.  **Install dependencies** from the `requirements.txt` file:
+
+    for macOS/Linux users:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+    for Windows users:
+    ```bash
+    # Install PyTorch, TorchAudio, and TorchVision for Windows
+    # replace cu121 with your CUDA version
+    # replace torchvision and torchaudio with your version
+    pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
+
+    # then install other dependencies
+    pip install -r requirements_win.txt
+    ```
+
 ## 🚀 Usage
 
 ![Demo Interface](fig/demo_interface.png)
@@ -173,6 +209,8 @@ python app.py
 python app.py --checkpoint_path /path/to/checkpoint --port 7865 --device_id 0 --share --bf16
 ```
 
+If you are MacOS, please use `--bf16 False` to avoid errors.
+
 #### 🛠️ Command Line Arguments
 
 - `--checkpoint_path`: Path to the model checkpoint (default: downloads automatically)

app.py

@@ -3,8 +3,8 @@ parser = argparse.ArgumentParser()
 parser.add_argument("--checkpoint_path", type=str, default="")
 parser.add_argument("--port", type=int, default=7865)
 parser.add_argument("--device_id", type=int, default=0)
-parser.add_argument("--share", action='store_true', default=False)
-parser.add_argument("--bf16", action='store_true', default=False)
+parser.add_argument("--share", type=bool, default=False)
+parser.add_argument("--bf16", type=bool, default=True)
 
 args = parser.parse_args()
 
@@ -31,7 +31,6 @@ def main(args):
         sample_data_func=data_sampler.sample,
     )
     demo.launch(
-        server_name="0.0.0.0",
         server_port=args.port,
         share=args.share
     )

pipeline_ace_step.py

@@ -158,7 +158,7 @@ class ACEStepPipeline:
         ])
         self.lang_segment = lang_segment
         self.lyric_tokenizer = VoiceBpeTokenizer()
-        text_encoder_model = UMT5EncoderModel.from_pretrained(text_encoder_checkpoint_path).eval()
+        text_encoder_model = UMT5EncoderModel.from_pretrained(text_encoder_checkpoint_path, torch_dtype=self.dtype).eval()
         text_encoder_model = text_encoder_model.to(device).to(self.dtype)
         text_encoder_model.requires_grad_(False)
         self.text_encoder_model = text_encoder_model
@@ -941,7 +941,7 @@ class ACEStepPipeline:
 
         output_path_flac = f"{base_path}/output_{time.strftime('%Y%m%d%H%M%S')}_{idx}.{format}"
         target_wav = target_wav.float()
-        torchaudio.save(output_path_flac, target_wav, sample_rate=sample_rate, format=format, backend="ffmpeg", compression=torchaudio.io.CodecConfig(bit_rate=320000))
+        torchaudio.save(output_path_flac, target_wav, sample_rate=sample_rate, format=format)
         return output_path_flac
 
     def infer_latents(self, input_audio_path):